Grid computing environments generally refer to distributed, networked computing models in which a plurality of computers are used collaboratively to solve one or more problems. Often, such problems to be solved are computationally intensive, and would require extensive and expensive resources to accomplish in a conventional computing arena. For example, problems such as the modeling of weather patterns or financial markets may be approached using grid computing, where such problems may be difficult and expensive to solve using a single computer. Thus, grid computing may provide a cost-effective, reliable way to approach computationally-intensive problems.
Compared to such computationally-intensive software applications, many software applications may alternatively (or additionally) be database intensive. That is, such applications may require large and/or frequent calls (e.g., queries) to a database. For example, in an enterprise context, it often may occur that the enterprise maintains a large, central database, which may store, for example, large amounts of data related to customers, employees, procedures, or other business-relevant data. Database-intensive applications may thus access the central database in order to perform associated functionality, such as generating customer reports, or processing the payroll of the enterprise. It may therefore be difficult to implement such database-intensive applications in a grid computing environment, since the benefits of using distributed computing resources, as in the grid computing model, may be diminished or outweighed by the time and resources necessary to access the required data within that context.